Late-stage clinical trial failures increase the overall cost and risk of bringing new drugs to market. Determining the pharmacokinetic (PK) drivers of toxicity and efficacy in preclinical studies and early clinical trials supports quantitative optimization of drug schedule and dose through computational modeling. Additionally, this approach permits prioritization of lead candidates with better PK properties early in development. Mylotarg is an antibody-drug conjugate (ADC) that attained U.S. Food and Drug Administration (FDA) approval under a fractionated dosing schedule after 17 years of clinical trials, including a 10-year period on the market resulting in hundreds of fatal adverse events. Although ADCs are often considered lower risk for toxicity due to their targeted nature, off-target activity and liberated payload can still constrain dosing and drive clinical failure. Under its original schedule, Mylotarg was dosed infrequently at high levels, which is typical for ADCs because of their long half-lives. However, our PK modeling suggests that these regimens increase maximum plasma concentration (Cmax)-related toxicities while producing suboptimal exposures to the target receptor. Our analysis demonstrates that the benefits of dose fractionation for Mylotarg tolerability should have been obvious early in the drug's clinical development and could have curtailed the proliferation of ineffective Phase III studies. We also identify schedules likely to be even more efficacious without compromising on tolerability. Alternatively, a longer-circulating Mylotarg formulation could obviate the need for dose fractionation, allowing superior patient convenience. Early-stage PK optimization through quantitative modeling methods can accelerate clinical development and prevent late-stage failures.
Keywords: Antibody-Drug Conjugate; Modeling; Pharmacodynamics; Pharmacokinetics; Schedule Optimization.
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